Data Characteristics
Data for rehabilitation device clinical trial pre-screening primarily originates from Electronic Medical Record (EMR) systems, rehabilitation therapy records, device operation logs, and patient follow-up reports. This data exists as a mix of structured and unstructured formats. Structured data includes basic patient information, diagnostic results, treatment plans, rehabilitation assessment scale scores (e.g., FIM, BI scales), device usage duration, and parameter settings. This data updates with each patient visit or device use. Unstructured data involves physician notes, therapist observations, and patient self-reports, existing as free text with irregular update frequencies. Fields may include proprietary data like device model, serial number, treatment mode, output power, and treatment duration, with units such as Watts (W), Hertz (Hz), and minutes (min). Document structures often include device manuals, operating instructions, and maintenance records, typically in PDF or DOCX format, containing extensive technical details and operational specifications.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The highly structured nature, mixed with some unstructured data, of rehabilitation device data requires knowledge base deployment to support both precise retrieval and semantic understanding. The continuous generation of device operation logs and treatment records necessitates incremental updates to the knowledge base and high real-time synchronization capabilities for data sources. The prevalence of PDF and DOCX documents means the file parsing module must have robust content extraction capabilities, especially for text within tables and images. The presence of specialized fields and units requires the tokenizer and entity recognition models to be optimized for the rehabilitation medicine domain. This prevents recall bias due to inaccurate recognition of professional terminology. During upgrades, new versions often introduce improved models or parsing algorithms. Ensure these updates seamlessly process existing data and validate compatibility with historical data. This avoids issues caused by data format or model mismatches. The deployment environment also needs sufficient storage and computing resources to handle these data volumes and processing requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Rehabilitation device manuals and operating instructions are often large. This ensures successful upload of single files. |
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances contextual completeness of rehabilitation records with retrieval efficiency. Avoids over-segmentation that loses semantic meaning. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures accuracy in clinical trial pre-screening. Recalls documents highly relevant to device performance and patient indicators. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing of large PDF/DOCX documents. Prevents parsing failures due to timeouts. |
maxContext | 32000 token | Includes more contextual information. Helps understand complex rehabilitation treatment plans and device interaction logic. |
Recall count (Recall Count) | Top 8 entries (top 8) | Considering the strong correlation between rehabilitation device parameters and patient indicators, increasing recall helps comprehensive evaluation. |
Common Pitfalls
- Knowledge base queries fail with a 404 error in logs. This usually indicates that the knowledge base index path or configuration was not correctly updated after an upgrade. The system cannot find the corresponding knowledge base resources.
- Uploading large rehabilitation device manuals results in prolonged unresponsiveness or parsing failure. This often occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too short, preventing the processing of complex documents. - Key device models or treatment parameters are not recalled in pre-screening results. This may be because the tokenizer is not optimized for rehabilitation medicine terminology, leading to inaccurate entity recognition.
Verification Steps
- Upload a rehabilitation device manual containing complex tables and images. Verify that it parses successfully and generates retrievable knowledge points.
- Query for specific rehabilitation device models or treatment plans multiple times. Check if the recalled results include the expected key parameters and treatment recommendations.
- Perform pre-screening tests using different types of patient rehabilitation records. Evaluate the system's understanding and matching accuracy for structured data and unstructured text. Adjust the
Similarity threshold(Similarity Threshold) based on actual needs.
The values provided are common starting points. Measure them against your own samples for optimal performance.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.